Hi, folks. And welcome to Module 13.
In this module, we will start
our discussion of logistic regression.
Logistic regression is appropriate
when we have a binary or dichotomous outcome.
This is an incredibly useful statistical technique
in public health practice
because we often have binary outcomes.
Things like, is the person dead or alive?
Recovered or not recovered? Diseased or not diseased?
All of these can be modeled using logistic regression.
We became familiar with these types of outcomes in Epi 1,
when we worked a lot with two-by-two tables.
But when we have a two-by-two table,
we only have one binary predictor as well.
Well, what logistic regression does
is allow us to model these binary outcomes
with multiple predictors,
just like we did with multiple linear regression,
so we can start controlling for confounding
and look at effect modification within our models.
In this module, we're going to stick
to just one predictor, which is simple logistic regression.
And the reason is that in order to create an equation
for our logistic regression models,
there's some algebra involved.
We wanna make sure we feel solid with that
before moving forward.
In the next module, we'll go ahead
and get into multiple logistic regression,
where we can start to look at multiple confounders,
effect modification, and all the things
you already learned in multiple linear regression.
Let me know if there are any questions, and take care.